1. Field of the Invention
The present invention relates to a system and method for detecting drowsiness in a driver. More particularly, the present invention is for a drowsy driver detection system with improved detection recognition.
2. Background of the Related Art
Driver drowsiness in commercial truck drivers is a major concern and is responsible for thousands of accidents and fatalities every year. In a 1994 report (Knipling 1994), the Office of Crash Avoidance Research (OCAR) of the National Highway Traffic Safety Administration (NHTSA) identified driver drowsiness as one of the leading causes of single and multiple car accidents. NHTSA estimates that 100,000 crashes annually involve driver fatigue resulting in more than 40,000 injuries. The Fatality Analysis Reporting System (FARS) estimates 1,544 fatalities due to driver drowsiness related accidents, each year. More than 3% of drowsiness related crashes (i.e. a total of 3,300 crashes and 84 fatalities) involved drivers of combination-unit trucks. Based on police reports, drowsiness accounts for 1% to 3% of all U.S. motor vehicle crashes (Lyznicki, Doege et al. 1998). The police report studies are likely to provide substantial underestimate as the drivers involved in fatigue accidents does not admit their state of drowsiness, and police may not investigate fatigue issues due to lack of time and knowledge.
Fatigue has been estimated in 15% of single vehicle fatal truck crashes (Wang and Knipling 1994) and is the most frequent contributor to crashes in which a truck driver is fatally injured (NTSB 1990). Based on NHTSA General Estimates System (GES) statistics (Knipling and Wierwille 1994), although the frequency of drowsiness related crashes involving passenger vehicles is greater than that of combination-unit trucks, the number of involvements per vehicle life cycle for trucks is about 4 times greater due to their very high exposure level, as well as the greater likelihood of night driving. Moreover, truck drowsy driver crashes are more severe in terms of injury and property damage (Wang and Knipling 1994). Long hours of continuous wakefulness, irregular driving schedules, night shifts, sleep disruption or fragmented sleep due to split off-duty time put truck drivers more at risk (Gander and James 1998), (Hamelin 1987), (McCartt, Rohrbaugh et al. 2000).
Driver's drowsiness can be measured by two classes of phenomena: Physical and physiological and Vehicle state variables. Physical and physiological measurements include the measurement of brain wave or Electroencephalogram (EEG) (Akerstedt and Gillberg 1990; Huang, Kuo et al. 1996), eye activity (Skipper, Wierwille et al. 1984; Dingus, Hardee et al. 1985; Ueno, Kaneda et al. 1994; Ogawa and Shimotani 1997). PERCLOS (PERcent eyelid CLOSure) is one of the most widely accepted measures in scientific literature for measurement and detection of drowsiness (Dinges, Mallis et al. 1998; Grace, Byrne et al. 1998).
Drowsiness detection systems have been developed which work based on measurement of Physical and physiological features, and can provide very good detection accuracy. However, they have some shortcomings. The problem with an EEG is that it requires the use of electrodes to be attached to the scalp and that makes it very impractical to use. Eye closure activity can also provide good detection accuracy, but capturing eye image unobtrusively can be expensive and challenging under certain conditions. Changes in light conditions, correction glasses, angle of face, and other conditions can seriously affect the performance of image processing systems.
With respect to Vehicle State Variables Measurement, other approaches for detecting driver drowsiness are based on monitoring driver inputs or vehicle output variables during driving. These methods have the advantage of being non-intrusive to the drivers. In this category, the focus of measurement is not on the condition of the driver, but on the performance output of the vehicle hardware. The vehicle control systems that might be monitored for sensing driving operation include the steering wheel, accelerator, and brake pedal. The vehicle parameters that can be measured include the vehicle speed, acceleration, yaw rate and lateral displacement. Since these techniques allow non-contact detection of drowsiness, they do not give the driver any feeling of discomfort. On the negative side, they are subject to numerous limitations depending on the vehicle type and driving conditions. Wierwille et al. (1992) discussed the performance measures as indicator of driver drowsiness in detail.
Researches indicate variables related to vehicle lane position show good correlation with drowsiness (Skipper, Wierwille et al. 1984), (Dingus, Hardee et al. 1985), (Pilutti and Ulsoy 1997). Since this research uses steering wheel data to detect drivers' drowsiness, this section will focus more on the previous studies and inventions regarding to using steering wheel data to detect drowsiness. Reference (Chaput, Petit et al. 1990) suggests that there exists some correlation between micro steering movements and drop in vigilance. Researchers (Elling and Sherman 1994) reported that steering wheel reversals and standard deviation of steering wheel angle are two measures that show some potential as drowsiness indicators. Other researchers (Fukuda, Akutsu et al. 1995) have developed a driver drowsiness detection system at the Toyota Motor Company. The authors used steering adjustment time to estimate drowsiness. In addition, phase plots of steering wheel angle verses steering wheel velocity can be used as an indicator of drowsiness (Siegmund, King et al. 1996).
A system that relies solely on steering inputs provides a number of benefits over the more common means of detecting drowsiness through eye-tracking or lane departure detection systems. A steering-only detection system is unobtrusive, capable of being implemented inexpensively with a minimal amount of additional sensors and computing power, and immune to problems associated with the dependency of other detection systems to the environment and weather such as performance degradation under low-light or rainy conditions.
A review of steering-based drowsiness detection systems is noted in Hartley, Horberry et al. 2000; Kircher, Uddman et al. 2002. Using vehicle steering activity as an indicator of drowsiness has been cited by many studies. Hulbert (1972) found that the sleep-deprived drivers have a lower frequency of steering reversals (every time steering angle crosses zero degree) than that of rested drivers. Researchers like Mast et al. (1966) and Dureman and Boden (1972) have found that there is a deterioration of steering performance with drowsiness. According to Kahneman (1973), effort and SWRR (Steering Wheel Reversing Rate) are linked. He showed that the SWRR decreases under the influence of substances such as alcohol, which reduces driver activation level. Ryder et al. (1981) found that the frequency of steering reversals decreases with time on task.
Yabuta et al. (1985) hypothesized that when a driver is drowsy or falling asleep his/her steering behavior becomes more erratic. Yabuta defined this erratic steering behavior as “more frequent steering maneuvers during wakeful periods, and no steering correction for a prolonged period of time followed by a jerky motion during drowsy periods.” Dingus et al. (1985) found that several steering related measures, such as steering velocity, steering wheel increment, and low velocity steering, can be used to predict drowsiness. Mackie and Wylie (1991) provided a review of patterns of steering wheel movements and vehicle speed. They have affirmed the complexity of the analysis of these two variables and reported that the environmental factors could highly affect the steering precision.
A study conducted by Chaput et al. (1990) suggests that there exists some correlation between micro steering movements and drop in vigilance. During high vigilance (alert) periods small amplitude steering wheel movements are frequent, but during fatigued periods large amplitude movements are more visible. Elling and Sherman (1994) analyzed actual driving data from one-hour of continuous driving by professional drivers. They reported that steering wheel reversals and standard deviation of steering wheel angle are two measures that show some potential as drowsiness indicators. They also reported that gap-size (i.e. the angle that the steering wheel must be reversed before being counted as a reversal) has a major influence on the reversal rate. Their gap-size function has a dead-band that disregards any extremely small reversals such as those due to road variations.
Fukuda et al. (1995) developed a driver drowsiness detection system at the Toyota Motor Company. The authors used steering adjustment time to estimate drowsiness. Their method consists of the following steps: (a) Steering adjustment intervals are calculated at different speeds for alert conditions (learning). These intervals vary with speed and individual behavior but it follows the same pattern. (b) The steering adjustment intervals are normalized at 80 km/hr (50 mph) speed. These intervals are constantly calculated. Whenever it reaches a threshold value, the driver is classified as drowsy. The value of drowsiness threshold is not constant but it varies with speed. The driving threshold is calculated by taking the product of the mean value of learned steering adjustment intervals in the normal state and the mean value of most recent steering adjustment intervals. The results show good correlation with EEG.
Siegmund et al. (1996) conducted an experiment based on the performance of 17 long haul truck drivers under alert and fatigued conditions on a closed circuit track. They presented a steering based set of weighing functions. These functions are based on steering angle and steering velocity. According to the researchers these weighing functions are correlated with EEGs and subjective evaluations of drivers. According to their findings, phase plots of steering wheel angle verses steering wheel velocity can be used as an indicator of drowsiness.
There is an on-going project so called SAVE (System for effective Assessment of the driver state and Vehicle control in Emergency situations) (Brookhuis, de Waard et al. 1998). The project aims to develop a demonstration prototype to identify driver impairment cause and classify it in one of the following categories: fatigue or sleep deprivation, alcohol or drug abuse, sudden illness of the driver and prolonged periods of inattention. The system is claimed to detect 90% of drowsy cases, but there is no formal report on the evaluation of the performance of the system. Sayed and Eskandarian (2001) developed an algorithm, which is based on Artificial Neural Network (ANN) learning of driver steering. They trained an ANN model using data from a driving simulator, driven by human subjects under various levels of sleep deprivation. The model identified drowsy and wake steering behavior, calculated over fixed period of time, with good accuracy.
The Center for Intelligent Systems Research (CISR), at The George Washington University, previously performed a series of experiments to develop a drowsiness detection algorithm, which is based on Artificial Neural Network (ANN) learning of driver's steering (Sayed and Eskandarian 2001; Sayed, Eskandarian et al. 2001a; Sayed, Eskandarian et al. 2001b). The development of the model was based on using the data from a passenger car driving simulator. Their model showed that steering activity can be used among other variables to indicate driver's drowsiness. Due to the difference between the dynamics of trucks as compared to cars and the professional skill level of commercial drivers, the effect of drowsiness on truck driving performance was not clear.